In this study, an improved gbest-PSO is proposed to overcome the shortcoming of
earlier convergence of classical gbest-PSO. Then the improved gbest-PSO is used to
identify the unknown inlet temperature profile in a plate channel flow. The effects of
measurement position and measurement error on the accuracy of prediction are studied
thoroughly. Analysis of computational results of two test problems shows that the improved
gbest-PSO proposed in this paper has an excellent smooth convergence characteristic.
The local refine mechanism introduced in the improved gbest-PSO increases the
opportunity of finding the global optimum greatly especially for high dimensional multimodal
optimization problems. Accurate results are obtained even when the measurements
contain a 10% noise. Consequently, the inverse convection heat transfer problem
is successfully solved by the improved gbest-PSO.

Received February 22, 2011; revised August 4, 2011; accepted August 31, 2011.
Communicated by I-Chen Wu.
* This work was supported by the National Natural Science Foundation of China, Young Scientists Fund (Grant
No. 51006121); also partially supported by Young Scientists Fund of NSF of China (51106049).